ArticleJournal of clinical medicine2020
Prediction of Resting Energy Expenditure in Children: May Artificial Neural Networks Improve Our Accuracy?
Article in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Comparative machine learning modeling of resting energy expenditure estimation in mechanically ventilated children after cardiac surgery.European journal of clinical nutrition · 2026Article
- Article
- AI-Enabled Precision Nutrition in the ICU: A Narrative Review and Implementation Roadmap.Nutrients · 2025Review
- Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children.Nutrients · 2021Article
- Personalized nutrition approach in pediatrics: a narrative review.Pediatric research · 2021Review
- Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients with influenza-like symptoms using only basic clinical data.Scandinavian journal of trauma, resuscitation and emergency medicine · 2020Article
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The inaccuracy of resting energy expenditure (REE) prediction formulae to calculate energy metabolism in children may lead to either under- or overestimated real caloric needs with clinical consequences. The aim of this paper was to apply artificial neural networks algorithms (ANNs) to REE prediction. We enrolled 561 healthy children (2-17 years). Nutritional status was classified according to World Health Organization (WHO) criteria, and 113 were obese. REE was measured using indirect calorimetry and estimated with WHO, Harris-Benedict, Schofield, and Oxford formulae. The ANNs considered specific anthropometric data to model REE. The mean absolute error (mean ± SD) of the prediction was 95.8 ± 80.8 and was strongly correlated with REE values (
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